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The Future is WearableThu, 07 Jan 2016 15:36:48 +0000en-UShourly1http://wordpress.org/?v=4.3The Best Laid Plans…http://wearabletech.moonbase.net/2015/02/24/the-best-laid-plans/
http://wearabletech.moonbase.net/2015/02/24/the-best-laid-plans/#commentsWed, 25 Feb 2015 00:17:40 +0000http://wearabletech.moonbase.net/?p=141Continue Reading]]>Last year Boeing (manufactures of large commercial jets for airlines) was implementing a plan for fiscal improvement that included heavy investments in its smaller 737 production pipeline, enabling them to build and ship 42+ 737s in a month (ultimately planning to scale to nearly 50 a month by 2017). Unfortunately, half a dozen fuselages that were manufactured for Boeing by Spirit Aerosystems were lost mid-year (along with some assemblies for 747 and 777 aircraft). The fuselages were shipping from Spirit’s Kanas manufacturing plant to Boeing via rail, but unfortunately the train derailed in a mountain pass in Montana, and all of Boeing’s shipment was lost. The rail line was closed for a period of time, which further impacted Boeing’s pipeline because there were typically 3-6 fuselanges in transit somewhere on the rail line to Boeing each day.

The inability to receive parts (due to weather, defects in parts received, shipping delays, or other production pipeline issues) can impact any type of manufacturing or assembly innovation.

]]>http://wearabletech.moonbase.net/2015/02/24/the-best-laid-plans/feed/0Innovations Drive Industryhttp://wearabletech.moonbase.net/2015/02/16/innovations-drive-industry/
http://wearabletech.moonbase.net/2015/02/16/innovations-drive-industry/#commentsMon, 16 Feb 2015 05:31:13 +0000http://wearabletech.moonbase.net/?p=132Continue Reading]]>The modern airline industry could not exist in the form it does today without a number of aviation innovations. The forces driving the adoption of these innovations include safety, consumer comfort, and economics for both the industry and for the consumer.

Late last year Gartner revealed a number of predictions regarding consumer adoption of wearable technologies in 2015 through 2018 and beyond. The company predicts a dramatic increase in sales of wearable devices, particularly fitness and biometric wearables (see the excerpt below), including smart clothing. Further prediction includes the sales of these devices through atypical channels such as wellness/health providers, gyms, insurance providers, weight loss companies, and even employers, including subsidized or discounted pricing.

Other market predictions:

2016: 40 percent of smartphones shipped to consumer will include biometric sensors

2017: 1/3 of consumers in emerging markets will not have ever owned a Windows device

2018: Virtual worlds will become mainstream rather than fringed, with over 25 million HMDs having been shipped

There are a number of forces that have synergistically combined to propel wearable technology. The social media phenomena and online sharing cultural has helped drive sharing self-quantification data (such as health/fitness and activity tracking), hence the popularity of devices such as the Fitbit.

The success of and popularity of smartphones as tables from Apple’s iOS platform and Google’s Android platform has knocked Microsoft Windows of its pedestal as the dominant technology platform. This is particularly true in geographical regions where PC penetration is lower, and many consumer have not owned (and likely never will) own a PC thanks to the lost cost of entry level smartphones now that they are available at less than $50.

Devices like the Oculus Rift and Google Glass have popularized and made wearable VR/AR devices accessible, as well as breaking down some of the cultural barriers towards their acceptance.

Wireless network are getting faster, and cloud-computing platforms are getting cheaper. The era of Affective Living, where our own cloud based personal assistant/advisor/intelligent tutoring system travels with us in our wearable devices and embedded devices in our environment (such as our house, workplace, automobiles, gym, and even personal aircraft) is closer than ever.

In 1993, British Airways was one of the largest and most profitable international passenger airline companies, and remains so more than a decade later. This continued success is in large part due to the introduction of scenario planning by DeAnne Julius, British Airway’s Chief Economist in 1994.

Julius proposed an experiment to the company’s Chairman’s Committee to see if scenario planning was suitable for use by airline: the incorporation of scenario planning into the British Airways annual business plan of April 1995 (Moyer, 1996).

The experiment took place in two phases. The initial phase was led by Julius, who directed a diverse development team comprised of staffers from multiple departments (Marketing, Corporate Strategy, & Government Affairs) and a ‘Halo Group’ of senior directors and senior managers who provided advice and feedback. In the first phase, the team focused on the determination of what the most significant external issues facing British Airways. Eleven significant issues were selected for further research in order to determine probable outcomes for each. Stories were written about the probably evolution of the issues over a ten year horizon (Moyer, 1996).
Phase two of the experiment was lead by Rod Muddle, the Head of Planning for British Airways in 1994. Scenario workshops were held by different groups within the airline (Engineering, Marketing, Corporate Strategy, Capacity Management, Cargo, etc.). Often the results from workshop were used to set the vision for another workshop (for example Engineering output set the stage for a Capacity Management scenario workshop).

The process was a long one, taking 7 months and over 200 man-years to complete. Challenges faced by the team included difficulty in building a database for the data model, in part due to difficulty acquiring and then modeling historical data, and the amount of on-going commitment required by the process was underestimated. In the end, it was found that workshops with 7-10 participants were most successful, with optimal participation by group members.

The experiment was a successful one. The scenarios developed were incorporated into the 1995 business plan for British Airways, and profited the company greatly. The management community within airline endorsed the continuation of scenario planning, and universally felt that it promoted cross division understand and collaboration, as well as broadening the global awareness within the company of (Moyer, 1996). Many of the scenarios dealt with possible changes in governance and potential impact of increased governance and profitability. The mitigation plans arising from continued scenario planning were one of the keys to British Airways continued profitability in the post 911 era when other airlines suffered devastating losses.

Whether considering wearable technology or business economics, the key requirement for successful innovation is predicting how the future will unfold. There are many different methodologies for planning and predicting the future. Two commonly used are traditional forecasting and scenario planning.

Traditional Forecasting attempts to predict the most likely future by using a forecasting model. Planners typically use mathematical extrapolation and mental extrapolation, utilizing the variables in the forecasting model to predict the likeliest scenario. There are several flaws in this approach. One is that it makes the assumption that nothing other than variables specified in the forecasting model will change. The use of historical data for variables in the model implies that tomorrow’s future will be nothing more than a variation of today (Wade, 2012, pp. 8-10). There is no single right projection that can be deduced from past data and behavior (Wack, 1985). One-dimensional traditional planning will fall short if the future deviates from the prediction and the planners do not have a response prepared for the unexpected and unpredicted future.

Scenario Planning’s methodology takes a different approach. Rather than trying to predict the single most likely scenario, it offers a range of alternate futures depending on how developments underway today unfold over time (Wade, 2012, p. 10).

This type of planning process requires a shift from mere extrapolation of forecast model variables. Planners frame the specific challenge to forecast, and then gather information and identify the driving forces that may result in significant change in the future This allows planners to identify point of uncertainty before generating possible scenarios and identifying appropriate responses to those scenarios (Wade, 2012, pp. 20-55).

Although scenario planning can be a more time consuming process than traditional forecasting, it allows for more flexibility. A chief advantage over traditional forecasting is that it supports planning and mitigation strategies for a range of futures.

World Association of Newspapers Scenario Planning. (n.d.). Retrieved February 2, 2015, from http://personalexpertsystem.blogspot.com/2013/08/world-association-of-newspapers.html

]]>http://wearabletech.moonbase.net/2015/02/04/planning-for-the-future/feed/0Adaptive Trendshttp://wearabletech.moonbase.net/2015/02/02/adaptive-trends/
http://wearabletech.moonbase.net/2015/02/02/adaptive-trends/#commentsMon, 02 Feb 2015 20:50:36 +0000http://wearabletech.moonbase.net/?p=100Continue Reading]]>The adoption of Affective Computing principles and data analytics are not limited to wearable technology and self-quantification. These principles are now emerging in the teaching and learning space. The New Media Consortium (2014) discusses the growth of data-driven learning and assessment, and how it will impact higher education in the next three to five years in its annual report on Higher Education.

In the early 1990s, customer relation management systems began to emerge, as companies started gathering information about customer preferences and customer behavior. The same analytics science user by companies to optimize profits for consumer product development and marketing is being adaptive to the science of learning analytics. Online learning environments in particular generate a tremendous amount of learning related data; by applying data mining techniques and statistical analysis to this data, it is possible to derive instructional decisions to optimize learning at many different target points, from subject areas down to individual students in a specific course.

Adaptive Learning Software is becoming quite sophisticated. In 2013 Pearson integrative adaptive learning into online courses, detecting patterns of student success and failure and applied customize tutoring services (New Media Consortium). Other experimental studies are even more sophisticated, moving beyond course success criteria. MIT’s Media Lab has conducted a number of studies in which student interest (i.e. affect) is measured using a variety of physiological indicate such as facial expressions, heart rate, and posture (Mota & Picard, 2003). The combination of real time course performance data and physiological monitoring to determine student affect and interest has the potential to bring truly intelligent tutoring systems to computer-based learning.

Data Privacy

One of the challenges faced by educational institutions is how to protect student privacy while using data to personalize the student experience. Universities are already beginning to formalize polices on how student learning data is gathered and used to make instructional decisions. Even Ivy League institutions like Harvard still have room for growth in this space. In a 2014 article published in the Boston Globe, it was released that Harvard ran a serious of clandestine experiments in which it secretly photographed 2,000+ students in lecture hall earlier this year without student consent. This led Harvard to implement new privacy and data gathering policies (Rocheleau, 2014).

Privacy policy is only on force impacting the development of intelligent tutoring systems. A universal and reusable standard for the integration of affective sensors (to measure student affect and emotion) into intelligent tutoring systems is needed. Progress is being made in this area, as evidenced by additional research at the MIT Media lab (Sidney et al., 2008), but additional work is needed before real-time emotion analysis of learners is viable.

]]>http://wearabletech.moonbase.net/2015/02/02/adaptive-trends/feed/0Errors and Innovationhttp://wearabletech.moonbase.net/2015/01/25/errors-and-innovation/
http://wearabletech.moonbase.net/2015/01/25/errors-and-innovation/#commentsMon, 26 Jan 2015 04:20:02 +0000http://wearabletech.moonbase.net/?p=83Continue Reading]]>Many great ideas and innovations come about as a result of error or failure. Aviation is a field where error sometimes results in lethal consequence. Soaring flight is a type of aviation with its own unique challenges and safety concerns. Soaring is a recreational (and sometimes competitive sport) in which pilots fly unpowered aircraft known as sailplanes or gliders. Sailplanes are typically towed aloft by a tow plane, and then released at an altitude of 1000′ to 2000′ feet. Sailplane pilots utilize lift (regions of rising air) to stay aloft, and often can fly hundreds of miles for many hours.

Figure 1: DG-300 being towed aloft.

Figure 2: DG-300 Sailplane

Figure 3: Pilot Reclined in Grob G-102 Sailplane

Sailplanes are designed to be aerodynamic, with narrow fuselages and long thing wings. One of the results of these design constraints is that pilots are typically seated in a very reclined position.Two risks in soaring flight are the risk of mid-air collision due to multiple gliders in close proximity in a region of lift. Another risk is structural failure due to flying in extremely turbulent air. In the event of an emergency from one of the above risks being realized, a sailplane pilot may need to rapidly bail out of the sailplane and parachute to safety. However, a fast exit from a sailplane may be impeded by a number of factors including:

Low reclined seating position

Narrow cockpit designs make lifting the upper body with arms only very difficult

Poor physical condition or injury to the pilot after a collision

High g forces such as those found in a spiral dive after a midair collision.

Consider that in a spiral dive following a midair collision, at a typical 2G acceleration a 120 pound pilot would be trying to lift 240 pounds out of the seat with just their upper arms in a very short time period.

DG Flugzeugbau, manufacturers of the DG series of sailplanes introduced an innovative safety system to address these concerns after a high profile fatal midair collision of involving a DG-800A sailplane collided with another glider. Although the DG-800 pilot was conscious following the collision, he was not able to bail out of his sailplane before impact with the ground.

The NOAH safety system is now available for DG and LS series sailplanes. In this system, an aircushion is built into the seat. It is activated using a single lever. Once activated, the seat belt is released, and the air cushion inflates to the height of the cockpit fuselage, allowing the pilot to easily just roll over the side of the aircraft to exit the cockpit (as seen in the photo below).

Figure 4: Demonstration of the NOAH system at the DG factory. Retrieved from http://www.dg-flugzeugbau.de/Data/noah7-m.jpg on January 19th, 2015.

The forces that drove this innovation were two fold. The idea behind the NOAH system was actually proposed many years before it was put into production by DG. It did not receive widespread support among the soaring community until after several high profile collisions in which pilots were known to have survived the collision but were unable to escape the aircraft. Fear of litigation might have been one motivation in the development of this system, community outrage might have been another. I believe DG had nothing more than the honorable goal of making soaring safer as their motivation which has been demonstrated by their willingness to help pilots retrofit their system into sailplanes manufactured by other companies.

Today the NOAH system a standard offering for DG sailplanes. In 2011, a proposal was made at the Soaring Society of American annual convention to require NOAH systems as all SSA sanctioned soaring events in addition to the already mandated parachute requirement. This proposal has yet to be enacted.

References

Johnson, S. (2010). Where Good Ideas Come From: The Natural History of Innovation.
Penguin UK.

]]>http://wearabletech.moonbase.net/2015/01/25/errors-and-innovation/feed/0Predicting Wearable Adoptionhttp://wearabletech.moonbase.net/2015/01/19/predicting-wearable-adoption/
http://wearabletech.moonbase.net/2015/01/19/predicting-wearable-adoption/#commentsTue, 20 Jan 2015 02:17:52 +0000http://wearabletech.moonbase.net/?p=76Continue Reading]]>The Delphi Method is a systematic problem solving and decision making technique developed to address complex problem using a group of subject matter experts, and was originally developed as a forecasting method (2007, Cialkowska et al).

The Delphi Method is based on the presumption that decision from an organized and structured group are more accurate than decisions from individuals or non-structured groups. It is an anonymous and iterative process overseen by a facilitator. The facilitator is responsible for gathering responses to surveys or forms from the anonymous participants, and analyses those responses to find common viewpoints and conflicting viewpoints. The process of gathering and analyzing responses is repeated until consensus is achieved.

The modified Delphi Method is similar iterative process. The difference is that the initial round is seeded with pre-selected information from vetted sources (such as literature reviews, interviews with subject matter experts, etc.) The purpose of the modification is to provide a strong foundation for the process using previous recognized work and to improve the first round response rate.

One of the potential downsides of the Delphi Method mentioned by Katherine Waiter in her thesis on wearable computing adoption (2003) is that predictions may be biased by the predictor’s personal interest in the technology or problem. One item of interest in her thesis was the use of the Delphi Method to predict technology adoption. One particular item of interest to me was the prediction that HMDs would start to see widespread adoption in the next 10-15 years. With the Oculus Rift, Samsung Gear VR, and Sony Mobius in or about to hit the marketplace, that particular prediction seemed right on target.

In his 2010 TED talk, Gary Wolf discusses the concept of “Self Quantification.” Wolf asserts that numbers can be used for more than advertising, governance, management, or even searches. Wolf believes that they can also be used by individuals as they “reflect, learn, remember and want to improve.”

He discusses the growing trend of individuals tracking self-information in the years leading up to his talk, beyond the typical (such as tracking weight on a scale). People began to track things on mobile devices such as food consumption, spending, moods, exercise, and even health issues and treatments (2010, Wolf).

At the time of Wolf’s talk, 1st generation fitness trackers were just beginning to emerge, such as the original Fitbit and Nike’s predecessor to today’s Nike Fuel Band, the Nike+ tracker. Since then, dozens of different health and fitness tracker have come to market, with a much wider array of sensors and data available than ever before. For a few hundred dollars, it is possible to wear a device that reliably tracks motion and exercise, continuous heart rate, electrodermal response (a.k.a. EDA), heart rate variability, skin temperature, GPS location, exercise intensity and duration, and sleep quantity and quality.

According to a 2014 Gartner Report, in 2013 over 73 million wearable devices were sold, and it is forecast that over 91 million wearable devices will be sold in 2016. The technological forces driving these innovative products are the diffusion of mobile devices into the populations, and the exponential improvements in processing speed, data storage, and biometric sensors.

By leveraging these new and emerging wearable devices to self quantify, individuals are able to empower themselves with a more complete understanding of self, allowing them to become more affective in their lives.

What I find most interesting in this video is that Wolf ‘s discussion of the growing phenomena of self-quantification is really the beginning of what I term “Affective Living.” Affective Living is the juxtaposition of Affective Computing and Ubiquitous Computing. Affective Computing is computing that recognizes, processes, and responds to human affect and emotion, while Ubiquitous Computing is computing anywhere and everywhere. As Affective Living is realized, technology and social forces around an individual will transparently adapt and respond to personal life experience. Affective Living will in turn empower more effective living, allowing individuals to have a greater effect in everything they attempt to accomplish.